Robotics Systems & Learning Engineer: Manipulation

Origami Robotics; San Francisco (CA); On-site; USD 120,000 - 180,000; Full time

Job summary

Origami Robotics in San Francisco is seeking a hands-on researcher to advance general-purpose dexterous manipulation on physical robots. You will train, deploy, and evaluate policies across learning, controls, software, and hardware, turning failures into better data, models, and systems.

You will build real-time control and state estimation, work with PyTorch or JAX, and collaborate across robot learning, perception, and planning to ship robust robotic systems in the real world.

Qualifications

• You have trained and deployed a learned policy on a physical robot.
• Strong Python and hands-on experience building and debugging robotic systems.
• Experience with PyTorch or JAX and methods such as imitation learning or RL.
• Working knowledge of robot kinematics, controls, and motion planning.
• Strong experimental judgment and ownership from idea to real-world demonstration.

Responsibilities

• Train, deploy, and improve dexterous manipulation policies on physical robots.
• Build teleoperation and data-collection workflows for training and evaluation.
• Apply imitation learning and RL fine-tuning where appropriate.
• Build custom real-time control and state estimation algorithms, integrate learned policies with perception and planning, and debug failures across the full robotic system.

Skills

Python

Robot systems debugging

PyTorch

JAX

Imitation learning

Reinforcement learning

Controls

Kinematics

Motion planning

Tools

ROS2

C++

Sim-to-real

Edge inference

Job description

Join a small, hands-on team building general-purpose dexterous manipulation. You’ll work across robot learning, controls, software, and hardware, test ideas quickly on physical robots, and turn failures into better data, models, and systems. Your work will directly shape what our robots can do.

In This Role You Will

• Train, deploy, and improve dexterous manipulation policies on physical robots.
• Build teleoperation and data-collection workflows for training and evaluation.
• Apply imitation learning and RL fine-tuning where appropriate.
• Build custom real-time control and state estimation algorithms, integrate learned policies with perception and planning, and debug failures across the full robotic system.

What We Hope You’ll Bring

• You have trained and deployed a learned policy on a physical robot.
• Strong Python and hands-on experience building and debugging robotic systems.
• Experience with PyTorch or JAX and methods such as imitation learning or RL.
• Working knowledge of robot kinematics, controls, and motion planning.
• Strong experimental judgment and ownership from idea to real-world demonstration.

Bonus Points If You Have

• Experience with dexterous or bimanual robots, including tactile or force sensing.
• Experience shipping real robotic systems using ROS2, C++, sim-to-real, or edge inference.
• Designed and built a robot or major subsystem through FTC, FRC, Formula SAE, Formula Hybrid, or a comparable project.

What To Include

• A robot-learning project you personally built or owned, with a video, repository, paper, or demo.
• A model or policy you trained, how you evaluated it on a physical robot, and a failure you diagnosed.